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        <h1 id="mysql-learning-note"><a href="#mysql-learning-note" class="headerlink" title="mysql learning note"></a>mysql learning note</h1><h2 id="性能"><a href="#性能" class="headerlink" title="性能"></a>性能</h2><p>查询原因：</p>
<ul>
<li>没有命中索引</li>
<li>没有分页处理</li>
</ul>
<h3 id="最大数据量"><a href="#最大数据量" class="headerlink" title="最大数据量"></a>最大数据量</h3><p>MySQL没有限制单位最大记录数，其取决于os以及文件系统对单文件最大的限制</p>
<h3 id="最大并发数"><a href="#最大并发数" class="headerlink" title="最大并发数"></a>最大并发数</h3><p>由maxconnections和maxuserconnections决定</p>
<p>maxconnections是指MySQL实例的最大连接数，上限值是16384，maxuserconnections是指每个数据库用户的最大连接数</p>
<p>MySQL会为每个连接提供缓冲区，意味着消耗更多的内存。如果连接数设置太高硬件吃不消，太低又不能充分利用硬件。一般要求两者比值超过10%，计算方法如下：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">max_used_connections / max_connections * 100% = 3/100 *100% ≈ 3%</span><br></pre></td></tr></table></figure></p>
<p>查看最大连接数与响应最大连接数：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">show variables like &apos;%max_connections%&apos;;</span><br><span class="line">show variables like &apos;%max_user_connections%&apos;;</span><br></pre></td></tr></table></figure></p>
<p>在配置文件my.cnf中修改最大连接数<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">[mysqld]max_connections = 100max_used_connections = 20</span><br></pre></td></tr></table></figure></p>
<h3 id="查询耗时0-5秒"><a href="#查询耗时0-5秒" class="headerlink" title="查询耗时0.5秒"></a>查询耗时0.5秒</h3><p>建议将单次查询耗时控制在0.5秒以内，0.5秒是个经验值，源于用户体验的3秒原则 。如果用户的操作3秒内没有响应，将会厌烦甚至退出。响应时间=客户端UI渲染耗时+网络请求耗时+应用程序处理耗时+查询数据库耗时，0.5秒就是留给数据库1/6的处理时间</p>
<h3 id="实施原则"><a href="#实施原则" class="headerlink" title="实施原则"></a>实施原则</h3><p>相比NoSQL数据库，MySQL容量小并发低、SQL约束太多。应用程序扩容比数据库要容易得多，所以实施原则是 <strong>数据库少干活，应用程序多干活</strong></p>
<ul>
<li>充分利用但不滥用索引，须知索引也消耗磁盘和CPU。</li>
<li>不推荐使用数据库函数格式化数据，交给应用程序处理。</li>
<li>不推荐使用外键约束，用应用程序保证数据准确性。</li>
<li>写多读少的场景，不推荐使用唯一索引，用应用程序保证唯一性。</li>
<li>适当冗余字段，尝试创建中间表，用应用程序计算中间结果，用空间换时间。</li>
<li>不允许执行极度耗时的事务，配合应用程序拆分成更小的事务。</li>
<li>预估重要数据表（比如订单表）的负载和数据增长态势，提前优化。</li>
</ul>
<h2 id="表设计"><a href="#表设计" class="headerlink" title="表设计"></a>表设计</h2><h3 id="数据类型"><a href="#数据类型" class="headerlink" title="数据类型"></a>数据类型</h3><p><strong>数据类型的选择原则：更简单或者占用空间更小</strong></p>
<ul>
<li>如果长度能够满足，整型尽量使用tinyint、smallint、medium_int而非int</li>
<li>如果字符串长度确定，采用char类型</li>
<li>如果varchar能够满足，不采用text类型</li>
<li>精度要求较高的使用decimal类型，也可以使用BIGINT，比如精确两位小数就乘以100后保存</li>
<li>尽量采用timestamp而非datetime</li>
</ul>
<table>
<thead>
<tr>
<th>类型</th>
<th>占据字节</th>
<th>描述</th>
</tr>
</thead>
<tbody>
<tr>
<td>datetime</td>
<td>8个字节</td>
<td>‘1000-01-01 00:00:00.000000’ to ‘9999-12-31 23:59:59.999999</td>
</tr>
<tr>
<td>timestamp</td>
<td>4字节</td>
<td>‘1970-01-01 00:00:01.000000’ to ‘2038-01-19 03:14:07.999999’</td>
</tr>
</tbody>
</table>
<h3 id="avoid-null-value"><a href="#avoid-null-value" class="headerlink" title="avoid null value"></a>avoid null value</h3><p>MySQL中字段为NULL时依然占用空间，会使索引、索引统计更加复杂。从NULL值更新到非NULL无法做到原地更新，容易发生索引分裂影响性能。尽可能将NULL值用有意义的值代替，也能避免SQL语句里面包含 is not null的判断</p>
<h3 id="text-type-Optimization"><a href="#text-type-Optimization" class="headerlink" title="text type Optimization"></a>text type Optimization</h3><p>由于text字段储存大量数据，表容量会很早涨上去，影响其他字段的查询性能。建议抽取出来放在子表里，用业务主键关联</p>
<h2 id="引索optimization"><a href="#引索optimization" class="headerlink" title="引索optimization"></a>引索optimization</h2><h3 id="index-分类"><a href="#index-分类" class="headerlink" title="index 分类"></a>index 分类</h3><ul>
<li>普通索引：最基本的索引。</li>
<li>组合索引：多个字段上建立的索引，能够加速复合查询条件的检索。</li>
<li>唯一索引：与普通索引类似，但索引列的值必须唯一，允许有空值。</li>
<li>组合唯一索引：列值的组合必须唯一。</li>
<li>主键索引：特殊的唯一索引，用于唯一标识数据表中的某一条记录，不允许有空值，一般用primary key约束。</li>
<li>全文索引：用于海量文本的查询，MySQL5.6之后的InnoDB和MyISAM均支持全文索引。由于查询精度以及扩展性不佳，更多的企业选择Elasticsearch。</li>
</ul>
<h3 id="index-optimization"><a href="#index-optimization" class="headerlink" title="index optimization"></a>index optimization</h3><ul>
<li>分页查询很重要，如果查询数据量超过30%，MYSQL不会使用索引。</li>
<li>单表索引数不超过5个、单个索引字段数不超过5个。</li>
<li>字符串可使用前缀索引，前缀长度控制在5-8个字符。</li>
<li>字段唯一性太低，增加索引没有意义，如：是否删除、性别。</li>
<li>合理使用覆盖索引，如下所示：<br>  select loginname, nickname from member where login_name = ?<br>  loginname, nickname两个字段建立组合索引，比login_name简单索引要更快</li>
</ul>
<h2 id="sql优化"><a href="#sql优化" class="headerlink" title="sql优化"></a>sql优化</h2><h3 id="batch"><a href="#batch" class="headerlink" title="batch"></a>batch</h3><p>当一条sql语句会处理整个表的数据的时候，有可能会堵塞其他sql运行<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">update status=0 FROMcoupon WHERE expire_date &lt;= #&#123;currentDate&#125; and status=1;</span><br></pre></td></tr></table></figure></p>
<p>因此可以使用分批操作</p>
<h4 id="optimization"><a href="#optimization" class="headerlink" title="optimization"></a>optimization</h4><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line">int pageNo = 1;</span><br><span class="line">int PAGE_SIZE = 100;</span><br><span class="line">while(true) &#123;</span><br><span class="line">    List&lt;Integer&gt; batchIdList = queryList(&apos;select id FROM `coupon` WHERE expire_date &lt;= #&#123;currentDate&#125; and status = 1 limit #&#123;(pageNo-1) * PAGE_SIZE&#125;,#&#123;PAGE_SIZE&#125;&apos;);</span><br><span class="line">    if (CollectionUtils.isEmpty(batchIdList)) &#123;</span><br><span class="line">        return;</span><br><span class="line">    &#125;</span><br><span class="line">    update(&apos;update status = 0 FROM `coupon` where status = 1 and id in #&#123;batchIdList&#125;&apos;)</span><br><span class="line">    pageNo ++;</span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>
<h3 id="operation-lt-gt-optimization"><a href="#operation-lt-gt-optimization" class="headerlink" title="operation &lt;&gt; optimization"></a>operation &lt;&gt; optimization</h3><p>通常&lt;&gt;操作符无法使用索引，举例如下，查询金额不为100元的订单：select id from orders where amount != 100;如果金额为100的订单极少，这种数据分布严重不均的情况下，有可能使用索引。鉴于这种不确定性，采用union聚合搜索结果，改写方法如下：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">(select id from orders where amount &gt; 100) union all(select id from orders where amount &lt; 100 and amount &gt; 0)</span><br></pre></td></tr></table></figure></p>
<h3 id="operation-or-optimization"><a href="#operation-or-optimization" class="headerlink" title="operation or optimization"></a>operation or optimization</h3><p>在Innodb引擎下or无法使用组合索引，比如：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">select id，product_name from orders where mobile_no = &apos;13421800407&apos; or user_id = 100;</span><br></pre></td></tr></table></figure></p>
<p>OR无法命中mobileno + userid的组合索引，可采用union，如下所示：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">(select id，product_name from orders where mobile_no = &apos;13421800407&apos;) union(select id，product_name from orders where user_id = 100);</span><br></pre></td></tr></table></figure></p>
<p>此时id和product_name字段都有索引，查询才最高效。</p>
<h3 id="operation-in-optimization"><a href="#operation-in-optimization" class="headerlink" title="operation in optimization"></a>operation in optimization</h3><ul>
<li>IN适合主表大子表小，EXIST适合主表小子表大。由于查询优化器的不断升级，很多场景这两者性能差不多一样了。</li>
<li>尝试改为join查询，举例如下：<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">select id from orders where user_id in (select id from user where level = &apos;VIP&apos;);</span><br><span class="line">采用JOIN如下所示：</span><br><span class="line">select o.id from orders o left join user u on o.user_id = u.id where u.level = &apos;VIP&apos;;</span><br></pre></td></tr></table></figure>
</li>
</ul>
<h3 id="不做列运算"><a href="#不做列运算" class="headerlink" title="不做列运算"></a>不做列运算</h3><p>通常在查询条件列运算会导致索引失效，如下所示：查询当日订单<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">select id from order where date_format(create_time，&apos;%Y-%m-%d&apos;) = &apos;2019-07-01&apos;;</span><br></pre></td></tr></table></figure></p>
<p>date_format函数会导致这个查询无法使用索引，改写后：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">select id from order where create_time between &apos;2019-07-01 00:00:00&apos; and &apos;2019-07-01 23:59:59&apos;;</span><br></pre></td></tr></table></figure></p>
<h3 id="avoid-Select-all"><a href="#avoid-Select-all" class="headerlink" title="avoid Select all"></a>avoid Select all</h3><p>如果不查询表中所有的列，避免使用 SELECT *，它会进行全表扫描，不能有效利用索引。</p>
<h3 id="operation-like-optimization"><a href="#operation-like-optimization" class="headerlink" title="operation like optimization"></a>operation like optimization</h3><p>like用于模糊查询，举个例子（field已建立索引）：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">SELECT column FROM table WHERE field like &apos;%keyword%&apos;;</span><br></pre></td></tr></table></figure></p>
<p>这个查询未命中索引，换成下面的写法：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">SELECT column FROM table WHERE field like &apos;keyword%&apos;;</span><br></pre></td></tr></table></figure></p>
<p>去除了前面的%查询将会命中索引，但是产品经理一定要前后模糊匹配呢？全文索引fulltext可以尝试一下，但Elasticsearch才是终极武器。</p>
<h3 id="operation-Join-optimization"><a href="#operation-Join-optimization" class="headerlink" title="operation Join optimization"></a>operation Join optimization</h3><p>join的实现是采用Nested Loop Join算法，就是通过驱动表的结果集作为基础数据，通过该结数据作为过滤条件到下一个表中循环查询数据，然后合并结果。如果有多个join，则将前面的结果集作为循环数据，再次到后一个表中查询数据。</p>
<ul>
<li>驱动表和被驱动表尽可能增加查询条件，满足ON的条件而少用Where，用小结果集驱动大结果集。</li>
<li>被驱动表的join字段上加上索引，无法建立索引的时候，设置足够的Join Buffer Size。</li>
<li>禁止join连接三个以上的表，尝试增加冗余字段。</li>
</ul>
<h3 id="oper-Limit-optimization"><a href="#oper-Limit-optimization" class="headerlink" title="oper Limit optimization"></a>oper Limit optimization</h3><p>limit用于分页查询时越往后翻性能越差，解决的原则：缩小扫描范围 ，如下所示：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">select * from orders order by id desc limit 100000,10 耗时0.4秒</span><br><span class="line">select * from orders order by id desc limit 1000000,10耗时5.2秒</span><br></pre></td></tr></table></figure></p>
<p>先筛选出ID缩小查询范围，写法如下：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">select * from orders where id &gt; (select id from orders order by id desc  limit 1000000, 1) order by id desc limit 0,10耗时0.5秒</span><br></pre></td></tr></table></figure></p>
<p>如果查询条件仅有主键ID，写法如下：<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">select id from orders where id between 1000000 and 1000010 order by id desc耗时0.3秒</span><br></pre></td></tr></table></figure></p>
<p>如果以上方案依然很慢呢？只好用游标了，感兴趣的朋友阅读JDBC使用游标实现分页查询的方法</p>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#mysql-learning-note"><span class="nav-number">1.</span> <span class="nav-text">mysql learning note</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#性能"><span class="nav-number">1.1.</span> <span class="nav-text">性能</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#最大数据量"><span class="nav-number">1.1.1.</span> <span class="nav-text">最大数据量</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#最大并发数"><span class="nav-number">1.1.2.</span> <span class="nav-text">最大并发数</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#查询耗时0-5秒"><span class="nav-number">1.1.3.</span> <span class="nav-text">查询耗时0.5秒</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#实施原则"><span class="nav-number">1.1.4.</span> <span class="nav-text">实施原则</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#表设计"><span class="nav-number">1.2.</span> <span class="nav-text">表设计</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#数据类型"><span class="nav-number">1.2.1.</span> <span class="nav-text">数据类型</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#avoid-null-value"><span class="nav-number">1.2.2.</span> <span class="nav-text">avoid null value</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#text-type-Optimization"><span class="nav-number">1.2.3.</span> <span class="nav-text">text type Optimization</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#引索optimization"><span class="nav-number">1.3.</span> <span class="nav-text">引索optimization</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#index-分类"><span class="nav-number">1.3.1.</span> <span class="nav-text">index 分类</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#index-optimization"><span class="nav-number">1.3.2.</span> <span class="nav-text">index optimization</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#sql优化"><span class="nav-number">1.4.</span> <span class="nav-text">sql优化</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#batch"><span class="nav-number">1.4.1.</span> <span class="nav-text">batch</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#optimization"><span class="nav-number">1.4.1.1.</span> <span class="nav-text">optimization</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#operation-lt-gt-optimization"><span class="nav-number">1.4.2.</span> <span class="nav-text">operation &lt;&gt; optimization</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#operation-or-optimization"><span class="nav-number">1.4.3.</span> <span class="nav-text">operation or optimization</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#operation-in-optimization"><span class="nav-number">1.4.4.</span> <span class="nav-text">operation in optimization</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#不做列运算"><span class="nav-number">1.4.5.</span> <span class="nav-text">不做列运算</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#avoid-Select-all"><span class="nav-number">1.4.6.</span> <span class="nav-text">avoid Select all</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#operation-like-optimization"><span class="nav-number">1.4.7.</span> <span class="nav-text">operation like optimization</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#operation-Join-optimization"><span class="nav-number">1.4.8.</span> <span class="nav-text">operation Join optimization</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#oper-Limit-optimization"><span class="nav-number">1.4.9.</span> <span class="nav-text">oper Limit optimization</span></a></li></ol></li></ol></li></ol></div>
            

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        dataType: isXml ? "xml" : "json",
        async: true,
        success: function(res) {
          // get the contents from search data
          isfetched = true;
          $('.popup').detach().appendTo('.header-inner');
          var datas = isXml ? $("entry", res).map(function() {
            return {
              title: $("title", this).text(),
              content: $("content",this).text(),
              url: $("url" , this).text()
            };
          }).get() : res;
          var input = document.getElementById(search_id);
          var resultContent = document.getElementById(content_id);
          var inputEventFunction = function() {
            var searchText = input.value.trim().toLowerCase();
            var keywords = searchText.split(/[\s\-]+/);
            if (keywords.length > 1) {
              keywords.push(searchText);
            }
            var resultItems = [];
            if (searchText.length > 0) {
              // perform local searching
              datas.forEach(function(data) {
                var isMatch = false;
                var hitCount = 0;
                var searchTextCount = 0;
                var title = data.title.trim();
                var titleInLowerCase = title.toLowerCase();
                var content = data.content.trim().replace(/<[^>]+>/g,"");
                
                var contentInLowerCase = content.toLowerCase();
                var articleUrl = decodeURIComponent(data.url).replace(/\/{2,}/g, '/');
                var indexOfTitle = [];
                var indexOfContent = [];
                // only match articles with not empty titles
                if(title != '') {
                  keywords.forEach(function(keyword) {
                    function getIndexByWord(word, text, caseSensitive) {
                      var wordLen = word.length;
                      if (wordLen === 0) {
                        return [];
                      }
                      var startPosition = 0, position = [], index = [];
                      if (!caseSensitive) {
                        text = text.toLowerCase();
                        word = word.toLowerCase();
                      }
                      while ((position = text.indexOf(word, startPosition)) > -1) {
                        index.push({position: position, word: word});
                        startPosition = position + wordLen;
                      }
                      return index;
                    }

                    indexOfTitle = indexOfTitle.concat(getIndexByWord(keyword, titleInLowerCase, false));
                    indexOfContent = indexOfContent.concat(getIndexByWord(keyword, contentInLowerCase, false));
                  });
                  if (indexOfTitle.length > 0 || indexOfContent.length > 0) {
                    isMatch = true;
                    hitCount = indexOfTitle.length + indexOfContent.length;
                  }
                }

                // show search results

                if (isMatch) {
                  // sort index by position of keyword

                  [indexOfTitle, indexOfContent].forEach(function (index) {
                    index.sort(function (itemLeft, itemRight) {
                      if (itemRight.position !== itemLeft.position) {
                        return itemRight.position - itemLeft.position;
                      } else {
                        return itemLeft.word.length - itemRight.word.length;
                      }
                    });
                  });

                  // merge hits into slices

                  function mergeIntoSlice(text, start, end, index) {
                    var item = index[index.length - 1];
                    var position = item.position;
                    var word = item.word;
                    var hits = [];
                    var searchTextCountInSlice = 0;
                    while (position + word.length <= end && index.length != 0) {
                      if (word === searchText) {
                        searchTextCountInSlice++;
                      }
                      hits.push({position: position, length: word.length});
                      var wordEnd = position + word.length;

                      // move to next position of hit

                      index.pop();
                      while (index.length != 0) {
                        item = index[index.length - 1];
                        position = item.position;
                        word = item.word;
                        if (wordEnd > position) {
                          index.pop();
                        } else {
                          break;
                        }
                      }
                    }
                    searchTextCount += searchTextCountInSlice;
                    return {
                      hits: hits,
                      start: start,
                      end: end,
                      searchTextCount: searchTextCountInSlice
                    };
                  }

                  var slicesOfTitle = [];
                  if (indexOfTitle.length != 0) {
                    slicesOfTitle.push(mergeIntoSlice(title, 0, title.length, indexOfTitle));
                  }

                  var slicesOfContent = [];
                  while (indexOfContent.length != 0) {
                    var item = indexOfContent[indexOfContent.length - 1];
                    var position = item.position;
                    var word = item.word;
                    // cut out 100 characters
                    var start = position - 20;
                    var end = position + 80;
                    if(start < 0){
                      start = 0;
                    }
                    if (end < position + word.length) {
                      end = position + word.length;
                    }
                    if(end > content.length){
                      end = content.length;
                    }
                    slicesOfContent.push(mergeIntoSlice(content, start, end, indexOfContent));
                  }

                  // sort slices in content by search text's count and hits' count

                  slicesOfContent.sort(function (sliceLeft, sliceRight) {
                    if (sliceLeft.searchTextCount !== sliceRight.searchTextCount) {
                      return sliceRight.searchTextCount - sliceLeft.searchTextCount;
                    } else if (sliceLeft.hits.length !== sliceRight.hits.length) {
                      return sliceRight.hits.length - sliceLeft.hits.length;
                    } else {
                      return sliceLeft.start - sliceRight.start;
                    }
                  });

                  // select top N slices in content

                  var upperBound = parseInt('1');
                  if (upperBound >= 0) {
                    slicesOfContent = slicesOfContent.slice(0, upperBound);
                  }

                  // highlight title and content

                  function highlightKeyword(text, slice) {
                    var result = '';
                    var prevEnd = slice.start;
                    slice.hits.forEach(function (hit) {
                      result += text.substring(prevEnd, hit.position);
                      var end = hit.position + hit.length;
                      result += '<b class="search-keyword">' + text.substring(hit.position, end) + '</b>';
                      prevEnd = end;
                    });
                    result += text.substring(prevEnd, slice.end);
                    return result;
                  }

                  var resultItem = '';

                  if (slicesOfTitle.length != 0) {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + highlightKeyword(title, slicesOfTitle[0]) + "</a>";
                  } else {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + title + "</a>";
                  }

                  slicesOfContent.forEach(function (slice) {
                    resultItem += "<a href='" + articleUrl + "'>" +
                      "<p class=\"search-result\">" + highlightKeyword(content, slice) +
                      "...</p>" + "</a>";
                  });

                  resultItem += "</li>";
                  resultItems.push({
                    item: resultItem,
                    searchTextCount: searchTextCount,
                    hitCount: hitCount,
                    id: resultItems.length
                  });
                }
              })
            };
            if (keywords.length === 1 && keywords[0] === "") {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-search fa-5x"></i></div>'
            } else if (resultItems.length === 0) {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-frown-o fa-5x"></i></div>'
            } else {
              resultItems.sort(function (resultLeft, resultRight) {
                if (resultLeft.searchTextCount !== resultRight.searchTextCount) {
                  return resultRight.searchTextCount - resultLeft.searchTextCount;
                } else if (resultLeft.hitCount !== resultRight.hitCount) {
                  return resultRight.hitCount - resultLeft.hitCount;
                } else {
                  return resultRight.id - resultLeft.id;
                }
              });
              var searchResultList = '<ul class=\"search-result-list\">';
              resultItems.forEach(function (result) {
                searchResultList += result.item;
              })
              searchResultList += "</ul>";
              resultContent.innerHTML = searchResultList;
            }
          }

          if ('auto' === 'auto') {
            input.addEventListener('input', inputEventFunction);
          } else {
            $('.search-icon').click(inputEventFunction);
            input.addEventListener('keypress', function (event) {
              if (event.keyCode === 13) {
                inputEventFunction();
              }
            });
          }

          // remove loading animation
          $(".local-search-pop-overlay").remove();
          $('body').css('overflow', '');

          proceedsearch();
        }
      });
    }

    // handle and trigger popup window;
    $('.popup-trigger').click(function(e) {
      e.stopPropagation();
      if (isfetched === false) {
        searchFunc(path, 'local-search-input', 'local-search-result');
      } else {
        proceedsearch();
      };
    });

    $('.popup-btn-close').click(onPopupClose);
    $('.popup').click(function(e){
      e.stopPropagation();
    });
    $(document).on('keyup', function (event) {
      var shouldDismissSearchPopup = event.which === 27 &&
        $('.search-popup').is(':visible');
      if (shouldDismissSearchPopup) {
        onPopupClose();
      }
    });
  </script>





  

  

  
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      else {
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